Gis And Rs Practical Machine Learning Tools And Techniques PDF Download
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| Author | : Dilan Thomas |
| Publisher | : Murphy & Moore Publishing |
| Total Pages | : 0 |
| Release | : 2023-09-26 |
| Genre | : Science |
| ISBN | : 9781639877454 |
Download GIS and Rs: Practical Machine Learning Tools and Techniques Book in PDF, ePub and Kindle
Machine learning (ML) refers to an artificial intelligence (AI) technique that teaches computers to learn from experiences. The algorithms of ML utilize computational techniques to learn information directly from data rather than using a preconceived equation as a model. ML is divided into two main categories, which include supervised learning and unsupervised learning. Each of them has diverse uses in geographic information system (GIS) and remote sensing (RS). ML is a key component of spatial analysis in GIS. It is extremely helpful for analyzing data in a variety of domains, including processing of satellite images. ML tools are primarily used in the processing of remote sensing data for interpretation, filtering and prediction. This book unravels the recent studies on machine learning tools and techniques for GIS and RS. As machine learning is emerging at a rapid pace, its contents will help the readers understand the modern concepts and applications of the subject. The book will serve as a valuable source of reference for graduate and postgraduate students.
| Author | : Hyung-Sup Jung |
| Publisher | : MDPI |
| Total Pages | : 438 |
| Release | : 2019-09-03 |
| Genre | : Technology & Engineering |
| ISBN | : 303921215X |
Download Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing Book in PDF, ePub and Kindle
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.
| Author | : Hyung-Sup Jung |
| Publisher | : |
| Total Pages | : 1 |
| Release | : 2019 |
| Genre | : Electronic books |
| ISBN | : 9783039212163 |
Download Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing Book in PDF, ePub and Kindle
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.
| Author | : Adegbola Ojo |
| Publisher | : CRC Press |
| Total Pages | : 269 |
| Release | : 2020-12-29 |
| Genre | : Science |
| ISBN | : 1000289370 |
Download GIS and Machine Learning for Small Area Classifications in Developing Countries Book in PDF, ePub and Kindle
Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work and undertake other social activities. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy related challenges in novel ways using area-based initiatives and evidence-based methods. This book exposes researchers, practitioners, and students to small area segmentation techniques for understanding, interpreting, and visualizing the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost effective ways. Using two developing countries as case studies, the book connects new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective with GIS and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples. Features: The first book to address the huge potential of small area segmentation for sustainable development, combining explanations of concepts, a range of techniques, and current applications. Includes case studies focused on core challenges that confront developing countries and provides thorough analytical appraisal of issues that resonate with audiences from the Global South. Combines GIS and machine learning methods for studying interrelated disciplines such as Demography, Urban Science, Sociology, Statistics, Sustainable Development and Public Policy. Uses a multi-method approach and analytical techniques of primary and secondary data. Embraces a balanced, chronological, and well sequenced presentation of information, which is very practical for readers.
| Author | : Saurabh Prasad |
| Publisher | : Elsevier |
| Total Pages | : 366 |
| Release | : 2024-06-01 |
| Genre | : Science |
| ISBN | : 044319078X |
Download Advances in Machine Learning and Image Analysis for GeoAI Book in PDF, ePub and Kindle
Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research. Covers the latest machine learning and signal processing techniques that can effectively leverage geospatial imagery at scale Presents a variety of algorithmic frameworks, including variants of convolutional neural networks, multi-stream networks, Bayesian networks, and more Includes open-source code-base for algorithms described in each chapter
| Author | : Hamid Reza Pourghasemi |
| Publisher | : Elsevier |
| Total Pages | : 798 |
| Release | : 2019-01-18 |
| Genre | : Mathematics |
| ISBN | : 0128156953 |
Download Spatial Modeling in GIS and R for Earth and Environmental Sciences Book in PDF, ePub and Kindle
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example
| Author | : Rémi Cresson |
| Publisher | : CRC Press |
| Total Pages | : 165 |
| Release | : 2020-07-15 |
| Genre | : Technology & Engineering |
| ISBN | : 100009359X |
Download Deep Learning for Remote Sensing Images with Open Source Software Book in PDF, ePub and Kindle
In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.
| Author | : Assefa M. Melesse |
| Publisher | : Elsevier |
| Total Pages | : 580 |
| Release | : 2019-07-03 |
| Genre | : Science |
| ISBN | : 0128159995 |
Download Extreme Hydrology and Climate Variability Book in PDF, ePub and Kindle
Extreme Hydrology and Climate Variability: Monitoring, Modelling, Adaptation and Mitigation is a compilation of contributions by experts from around the world who discuss extreme hydrology topics, from monitoring, to modeling and management. With extreme climatic and hydrologic events becoming so frequent, this book is a critical source, adding knowledge to the science of extreme hydrology. Topics covered include hydrometeorology monitoring, climate variability and trends, hydrological variability and trends, landscape dynamics, droughts, flood processes, and extreme events management, adaptation and mitigation. Each of the book's chapters provide background and theoretical foundations followed by approaches used and results of the applied studies. This book will be highly used by water resource managers and extreme event researchers who are interested in understanding the processes and teleconnectivity of large-scale climate dynamics and extreme events, predictability, simulation and intervention measures. Presents datasets used and methods followed to support the findings included, allowing readers to follow these steps in their own research Provides variable methodological approaches, thus giving the reader multiple hydrological modeling information to use in their work Includes a variety of case studies, thus making the context of the book relatable to everyday working situations for those studying extreme hydrology Discusses extreme event management, including adaption and mitigation
| Author | : Wang, John |
| Publisher | : IGI Global |
| Total Pages | : 4092 |
| Release | : 2008-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 159904952X |
Download Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications Book in PDF, ePub and Kindle
In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.
| Author | : Shammunul Islam |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 347 |
| Release | : 2018-11-30 |
| Genre | : Computers |
| ISBN | : 1788996984 |
Download Hands-On Geospatial Analysis with R and QGIS Book in PDF, ePub and Kindle
Practical examples with real-world projects in GIS, Remote sensing, Geospatial data management and Analysis using the R programming language Key FeaturesUnderstand the basics of R and QGIS to work with GIS and remote sensing dataLearn to manage, manipulate, and analyze spatial data using R and QGISApply machine learning algorithms to geospatial data using R and QGISBook Description Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You’ll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you’ll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you’ll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You’ll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You’ll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you’ll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you’ll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time. What you will learnInstall R and QGISGet familiar with the basics of R programming and QGISVisualize quantitative and qualitative data to create mapsFind out the basics of raster data and how to use them in R and QGISPerform geoprocessing tasks and automate them using the graphical modeler of QGISApply different machine learning algorithms on satellite data for landslide susceptibility mapping and predictionWho this book is for This book is great for geographers, environmental scientists, statisticians, and every professional who deals with spatial data. If you want to learn how to handle GIS and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful but is not necessary.
